HPO-B

Problem Difficulty Classification

By default, the dataset is split into a fixed training set and a testing set.

Set Type	Source Data	Number of Problems
Training Set	meta_train_data	758
Testing Set	meta_vali_data + meta_test_data	177

Note: If difficulty is set to ‘all’, the training and testing sets are merged, containing all 935 problems.

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HPO-B is an autoML hyper-parameter optimization benchmark which includes a wide range of hyperparameter optimization tasks for 16 different model types (e.g., SVM, XGBoost, etc.), resulting in a total of 935 problem instances. The dimension of these problem instances range from 2 to 16. We also note that HPO-B represents problems with ill-conditioned landscape such as huge flattern.

• Paper：”Hpo-b: A large-scale reproducible benchmark for black-box hpo based on openml.” arXiv preprint arXiv:2106.06257 (2021).

• Code Resource： HPO-B